A Generative and Mutational Approach for Synthesizing Bug-Exposing Test Cases to Guide Compiler Fuzzing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Random test case generation, or fuzzing, is a viable means for uncovering compiler bugs. Unfortunately, compiler fuzzing can be time-consuming and inefficient with purely randomly generated test cases due to the complexity of modern compilers. We present COMFUZZ, a focused compiler fuzzing framework. COMFUZZ aims to improve compiler fuzzing efficiency by focusing on testing components and language features that are likely to trigger compiler bugs. Our key insight is human developers tend to make common and repeat errors across compiler implementations; hence, we can leverage the previously reported buggy-exposing test cases of a programming language to test a new compiler implementation. To this end, COMFUZZ employs deep learning to learn a test program generator from open-source projects hosted on GitHub. With the machine-generated test programs in place, COMFUZZ then leverages a set of carefully designed mutation rules to improve the coverage and bug-exposing capabilities of the test cases. We evaluate COMFUZZ on 11 compilers for JS and Java programming languages. Within 260 hours of automated testing runs, we discovered 33 unique bugs across nine compilers, of which 29 have been confirmed and 22, including an API documentation defect, have already been fixed by the developers. We also compared COMFUZZ to eight prior fuzzers on four evaluation metrics. In a 24-hour comparative test, COMFUZZ uncovers at least 1.5× more bugs than the state-of-the-art baselines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it